There’s a lot of talk these days about how computers are becoming smarter. Many people are working on this problem in different ways. Some of them are using a method called machine learning. Instead of programming a computer to do something specific, they give the computer a lot of data and tell it to figure out how to do the task on its own. It’s sort of like a child learning how to walk by trial and error.
Over a period of time, the computer figures out the best way to complete the task. Machine learning is cool because it can help computers get really good at tasks that are hard to program, like recognizing faces in photos or understanding what people are saying. The more data the computer has, the better it can learn.
But, the problem with machine learning is that it needs a lot of data to work well. This can be hard to get, especially for tasks that are new or uncommon. This is where “inductive transfer” comes in. It’s a fancy term for a simple idea: instead of starting from scratch each time, let the computer use what it has already learned to help it learn new things. For example, if a computer has learned how to recognize cats, it can use that knowledge to help it learn how to recognize dogs. Inductive transfer can help computers learn faster and better.
There are different ways to do inductive transfer. One is called “multi-task learning”. This is when a computer learns several tasks at the same time, using the same data. For instance, if a computer is learning to recognize both cats and dogs, it can use the same pictures to learn both tasks. Another way is “sequential transfer learning”. This is when a computer uses what it has learned from one task to help it learn another task. For example, if a computer has learned how to recognize cats, it can use that knowledge to help it learn how to recognize dogs.
Machine learning and inductive transfer can help us build computers that are really smart and can do lots of useful things. But, we need to be careful. Computers can make mistakes, and those mistakes can have real consequences. For example, if a computer incorrectly identifies a person’s face, it could accidentally let someone in who shouldn’t be there or charge the wrong person for a crime. We need to make sure we understand how the computer is making its decisions, and we need to check its work. This way, we can use the benefits of machine learning and inductive transfer, while minimizing the risks.
Register your new business name at register.biz.au
